In order to control a fleet of unmanned surface vehicles, or USVs, in an environment that contains unknown obstacles it is not sufficient to simply use a path-planning algorithm designed for a single USV. Navigation and goal-finding behavior must work in such a way that a fleet of USVs discover, share, and cooperatively negotiate obstacles.
In addition, operation in an electromagnetic-spectrum limited environment means that there is no guaranteed or reliable ability to “reach-back” communication to a central base station or processing node. Since it is not efficient to pre-plan precise fleet movements for every type of path and any obstacle, a real-time and distributed system solution is needed. Another constraint imposed by electromagnetic-spectrum limits are that individual directions cannot be calculated and handed out; each USV must have an intuition of where to go and how fast to get there while considering its own location amongst the fleet of other USVs and obstacles.
A strategy that works well for a single USV is the use of artificial potential fields (APF). Virtual forces are felt or sensed by the USV. This results in the USV moving away from obstacles and towards waypoints. This concept is used by the USV fleet but it does not solely guarantee mission assurance. Local minimums, where the sum of all virtual forces in a location is equal to zero, are a well-known problem in this approach. If a USV solely relies on APF to navigate and then gets trapped in a local minim, the USV will not have the virtual motivation to continue towards the goal. For this reason, APF guidance alone is not a sufficient navigation strategy.